We have gathered data from Ecommerce website sales for 3 days. The goal of our analysis is to segment our customers

Importing the data

First step is of course to import the data.

Here swe have two data files:

  • “Anonymized_transactions_all.csv” which compiles all transactions that happened between January 1st and February 7th. Each row of the file corresponds to the purchase of one item (when various items are bought during the same transaction, several lines are created). The different fields listed in the file are:
  • Order number: unique identification of the order
  • SKU number: identifier of an item in the ecommerce database
  • Unit price of the item purchased
  • Price paid for the item (can be different from the item price if there are promotions for example)
  • Date of the order
  • Time of the order
  • Payment method used
  • Price paid for the whole order

  • Anonymized name of the customer (“Anonym”+several digits)

  • “Categories_all.csv” contains a breakdown of each item into sub categories. Each line corresponds to an item that can be purchased through the ecommerce website. The different fields are:
  • SKU number: the unique identifier for each item
  • Item description: a sentence describing the item
  • Category of the item
  • Sub-category of the item

  • Sub-sub-category of the item

# This simply reads the CSV files and creates two variables containing text
# data
Transaction_data <- read.csv(Transaction_datafile_name, sep = ";")
Categories_data <- read.csv(Categories_datafile_name, sep = ";")



# For later use, we have to remove the items that don't have any category
# from the dataset Variable SKU_Cat will be a vector containing the
# categories and whose names are the SKUs present in the datafile
# 'Categories_all.csv'
SKU_Cat <- Categories_data[, "Category"]
names(SKU_Cat) <- Categories_data[, "SKU"]

# We have to exclude some customers, linked to fake accounts (created for
# debug purposes by the ecommerce website)

Customer_blacklist = c("Customer254", "Customer262", "Customer302", "Customer9869")
Transaction_blacklist = NULL

# Next, let's add to this blacklist the transactions involving SKU not
# present in the datafile 'Categories_all.csv'
for (i in 1:nrow(Transaction_data)) {
    if (!(as.character(Transaction_data[i, "SKU"]) %in% names(SKU_Cat)) || (as.character(Transaction_data[i, 
        "Anonym"]) %in% Customer_blacklist)) {
        Transaction_blacklist = c(Transaction_blacklist, i)
    }
}

# we keep only the ones that are not on the blacklist
Transaction_data <- Transaction_data[-Transaction_blacklist, ]


nbTransactions = nrow(Transaction_data)

After having imported the data, the idea is to create a new variable, that sums up the items bought by each customer.

# We have to figure out at first the list of customers and of items sold
Customer_list = as.character(Transaction_data[1, "Anonym"])
ItemsSold_list = as.character(Transaction_data[1, "SKU"])
Orders_list = as.character(Transaction_data[1, "Order.Number"])
for (i in 2:nbTransactions) {
    if (!(as.character(Transaction_data[i, "Anonym"]) %in% Customer_list)) {
        Customer_list <- c(Customer_list, as.character(Transaction_data[i, "Anonym"]))
    }
    if (!(as.character(Transaction_data[i, "SKU"]) %in% ItemsSold_list)) {
        ItemsSold_list <- c(ItemsSold_list, as.character(Transaction_data[i, 
            "SKU"]))
    }
    if (!(as.character(Transaction_data[i, "Order.Number"]) %in% Orders_list)) {
        Orders_list <- c(Orders_list, as.character(Transaction_data[i, "Order.Number"]))
    }
}

# The number of unique customers and different items sold is then easy to
# compute
nbCustomers = length(Customer_list)
nbItemsSold = length(ItemsSold_list)
nbOrders = length(Orders_list)

# Then we create a matrix that we will populate with the actual transactions
Sales_Cust_Items = matrix(0 * 1:(nbCustomers * nbItemsSold), ncol = nbItemsSold, 
    nrow = nbCustomers)
colnames(Sales_Cust_Items) <- ItemsSold_list
rownames(Sales_Cust_Items) <- Customer_list

for (i in 1:nbTransactions) {
    itemsold = as.character(Transaction_data[i, "SKU"])
    customer = as.character(Transaction_data[i, "Anonym"])
    Sales_Cust_Items[customer, itemsold] <- Sales_Cust_Items[customer, itemsold] + 
        1
}

# And we create a matrix that we will populate with the actual orders
Sales_Cust_Orders = matrix(0 * 1:(nbCustomers * nbOrders), ncol = nbOrders, 
    nrow = nbCustomers)
colnames(Sales_Cust_Orders) <- Orders_list
rownames(Sales_Cust_Orders) <- Customer_list

for (i in 1:nbTransactions) {
    order = as.character(Transaction_data[i, "Order.Number"])
    customer = as.character(Transaction_data[i, "Anonym"])
    Sales_Cust_Orders[customer, order] <- Sales_Cust_Orders[customer, order] + 
        1
}

We have created 2 variables: Sales_Cust_Items and Sales_Cust_Orders

Basic data visualizations

Distribution of the number of items ordered per customer

A total of 6698 different items have been sold over the period (counting only once those who have been sold several times).

## Warning: Removed 56 rows containing non-finite values (stat_bin).

## Warning: Removed 56 rows containing non-finite values (stat_bin).

Distribution of number of orders per customer

A total of 16098 orders have been made over the period.

The majority of customers have only made 1 order.

## Warning: Removed 4 rows containing non-finite values (stat_bin).

Top 10 customers (by number of items ordered)

Top 10 customers bought 8.52% of all items sold over the period

Top 10 SKUs (by number of items ordered)

Top 10 SKUs represent 31.26% of all items sold over the period

Determination of the most important factors

The goal of our analysis is to build an algorithm that would suggest other purchases to existing customers.

Our data goes from January 1st to February 7th. We will divide the dataset in two: * First dataset regroups people who bought items in January and in February * Second dataset regroups people who ordered more than one items during January, excluding the customers who are already in the first dataset.

We will use the second dataset to buid a segmentation of the customer based on the items they bought, and then use the second dataset to check if the segmentation is accurate enough to be predicitve: we will assign the customers from the first dataset in segments based on their purchases in January, and try then to anticipate their purchases in February.

Data vizualization

## Warning: Removed 27 rows containing non-finite values (stat_bin).

Analysis of the results

TO BE COMPLETED

Use of categories instead of item name

To make our computations easier, we can associate each item to its category, thus drastically reducing the number of columns in the matrix.

PCA analysis

Let’s look at the variance explained as well as the eigenvalues :

Eigenvalue Pct of explained variance Cumulative pct of explained variance
Component 1 1.46 12.16 12.16
Component 2 1.16 9.67 21.83
Component 3 1.11 9.25 31.09
Component 4 1.09 9.11 40.19
Component 5 1.08 9.03 49.22
Component 6 1.00 8.31 57.53
Component 7 0.98 8.18 65.71
Component 8 0.96 8.02 73.73
Component 9 0.90 7.53 81.26
Component 10 0.88 7.31 88.57
Component 11 0.75 6.25 94.82
Component 12 0.62 5.18 100.00

We think that we can be a bit more accurate than these results, so we’ll try to do the same computations with the subcategories.

Use of Sub-categories

Not every item has a sub_category in the file “Categories_all.csv”, so we will define the subcategory as the concatenation of the Category and the Subcategory found in the datafile.

It means that if an item doesn’t have a subcategory, it will be referenced by its category.

Top 15 subcategories account for 63.7% of sales in volume

PCA analysis

Let’s look at the variance explained as well as the eigenvalues :

Eigenvalue Pct of explained variance Cumulative pct of explained variance
Component 1 2.21 2.28 2.28
Component 2 1.91 1.97 4.25
Component 3 1.77 1.82 6.07
Component 4 1.58 1.63 7.70
Component 5 1.53 1.58 9.28
Component 6 1.51 1.56 10.84
Component 7 1.47 1.51 12.36
Component 8 1.42 1.47 13.82
Component 9 1.36 1.40 15.23
Component 10 1.32 1.36 16.58
Component 11 1.30 1.34 17.92
Component 12 1.25 1.29 19.21
Component 13 1.23 1.27 20.48
Component 14 1.22 1.26 21.74
Component 15 1.19 1.23 22.97
Component 16 1.18 1.21 24.18
Component 17 1.17 1.20 25.39
Component 18 1.16 1.19 26.58
Component 19 1.15 1.18 27.76
Component 20 1.14 1.17 28.93
Component 21 1.13 1.17 30.10
Component 22 1.12 1.15 31.25
Component 23 1.11 1.14 32.39
Component 24 1.10 1.14 33.53
Component 25 1.09 1.13 34.66
Component 26 1.08 1.11 35.77
Component 27 1.08 1.11 36.88
Component 28 1.07 1.10 37.98
Component 29 1.07 1.10 39.08
Component 30 1.06 1.09 40.18
Component 31 1.05 1.09 41.27
Component 32 1.05 1.08 42.35
Component 33 1.04 1.07 43.42
Component 34 1.04 1.07 44.50
Component 35 1.04 1.07 45.56
Component 36 1.03 1.06 46.62
Component 37 1.02 1.05 47.68
Component 38 1.02 1.05 48.72
Component 39 1.01 1.04 49.77
Component 40 1.01 1.04 50.81
Component 41 1.01 1.04 51.85
Component 42 1.01 1.04 52.89
Component 43 1.00 1.04 53.93
Component 44 1.00 1.03 54.96
Component 45 1.00 1.03 55.99
Component 46 1.00 1.03 57.02
Component 47 0.99 1.02 58.04
Component 48 0.99 1.02 59.06
Component 49 0.99 1.02 60.08
Component 50 0.98 1.01 61.09
Component 51 0.98 1.01 62.09
Component 52 0.97 1.00 63.10
Component 53 0.96 0.99 64.09
Component 54 0.96 0.99 65.08
Component 55 0.96 0.99 66.07
Component 56 0.95 0.98 67.05
Component 57 0.95 0.97 68.03
Component 58 0.94 0.97 69.00
Component 59 0.94 0.97 69.96
Component 60 0.93 0.96 70.92
Component 61 0.93 0.95 71.88
Component 62 0.92 0.95 72.83
Component 63 0.90 0.93 73.76
Component 64 0.90 0.93 74.69
Component 65 0.90 0.93 75.61
Component 66 0.88 0.91 76.52
Component 67 0.88 0.91 77.43
Component 68 0.87 0.90 78.33
Component 69 0.87 0.90 79.23
Component 70 0.86 0.89 80.12
Component 71 0.86 0.88 81.00
Component 72 0.85 0.88 81.88
Component 73 0.84 0.87 82.75
Component 74 0.83 0.86 83.60
Component 75 0.82 0.85 84.45
Component 76 0.81 0.84 85.29
Component 77 0.81 0.84 86.13
Component 78 0.80 0.83 86.95
Component 79 0.79 0.82 87.77
Component 80 0.78 0.80 88.57
Component 81 0.77 0.79 89.36
Component 82 0.76 0.78 90.15
Component 83 0.75 0.78 90.93
Component 84 0.74 0.77 91.69
Component 85 0.73 0.76 92.45
Component 86 0.73 0.75 93.20
Component 87 0.73 0.75 93.95
Component 88 0.71 0.73 94.68
Component 89 0.70 0.72 95.40
Component 90 0.69 0.71 96.10
Component 91 0.64 0.66 96.77
Component 92 0.63 0.65 97.42
Component 93 0.61 0.63 98.04
Component 94 0.53 0.55 98.59
Component 95 0.50 0.51 99.10
Component 96 0.49 0.51 99.61
Component 97 0.38 0.39 100.00

Interpret the factors

Loking at the graph and table above, we chose to take the top 40 factors, to explain at least 50% of the variance.

To better visualise them, we will use the “varimax” rotation. For our data, the 40 selected factors look as follows after this rotation:

Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22 Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28 Comp.29 Comp.30 Comp.31 Comp.32 Comp.33 Comp.34 Comp.35 Comp.36 Comp.37 Comp.38 Comp.39 Comp.40
Cameras_DSLR/SLR 0.82 0.01 0.01 0.00 0.01 -0.01 0.01 -0.01 0.02 -0.01 0.02 -0.01 0.00 0.01 0.01 -0.01 0.18 -0.01 0.01 0.00 0.00 -0.01 -0.02 0.00 -0.01 0.00 -0.01 0.01 0.00 0.01 -0.05 0.01 0.01 -0.03 0.01 0.06 -0.01 0.02 -0.03 0.00
Computers & Laptops_Laptops 0.66 0.00 0.01 0.00 0.00 0.01 0.01 -0.01 0.02 -0.02 0.00 -0.02 0.01 0.00 0.02 -0.01 0.09 0.00 0.01 -0.02 -0.03 -0.03 -0.01 0.00 0.01 -0.01 -0.01 0.00 0.00 0.01 -0.01 0.01 0.00 -0.05 0.03 0.00 -0.04 0.00 -0.01 -0.01
Cameras_Point & Shoot (plain digital) 0.64 -0.01 -0.01 -0.01 -0.02 -0.01 -0.03 -0.01 -0.05 0.05 -0.01 -0.01 0.00 -0.01 -0.01 -0.05 0.04 0.02 -0.02 0.00 0.04 0.01 0.06 -0.01 -0.01 -0.01 -0.03 -0.03 -0.01 -0.05 -0.04 -0.02 0.01 0.11 -0.08 -0.02 0.08 -0.02 0.07 0.01
Computers & Laptops_Computer Accessories 0.40 -0.04 -0.01 0.00 0.00 0.01 -0.03 0.07 0.01 -0.05 -0.04 0.09 -0.04 -0.02 -0.01 0.04 -0.08 -0.04 -0.02 0.02 0.04 0.19 0.04 -0.04 0.01 0.02 0.01 0.04 -0.04 0.09 0.03 -0.02 -0.07 0.06 0.06 -0.07 0.06 0.42 -0.02 0.03
Mobiles & Tablets_Landline Phones 0.40 0.02 -0.01 -0.01 0.00 0.02 -0.01 -0.01 0.00 -0.01 -0.01 0.01 -0.01 0.00 -0.02 0.04 -0.03 0.03 0.00 0.07 -0.03 -0.01 -0.04 0.01 0.05 -0.01 0.03 0.04 0.03 0.02 -0.01 0.00 -0.04 -0.02 0.04 0.47 0.01 -0.03 -0.01 0.01
Computers & Laptops_Tablets 0.23 -0.02 0.00 -0.01 -0.01 -0.03 -0.02 -0.01 -0.01 0.00 0.00 0.00 0.00 0.00 -0.02 0.02 0.56 0.01 0.01 -0.01 -0.03 0.04 -0.05 0.06 -0.03 0.07 -0.01 -0.02 -0.04 -0.03 0.14 -0.01 0.04 -0.01 -0.01 0.00 -0.04 0.02 -0.04 -0.03
Mobiles & Tablets_Tablets 0.21 -0.02 -0.02 -0.01 -0.03 -0.05 -0.05 -0.01 -0.04 -0.03 -0.02 -0.04 -0.04 -0.02 0.03 -0.01 0.58 -0.05 -0.02 -0.04 0.01 -0.09 -0.07 -0.03 -0.03 -0.01 0.01 -0.04 -0.01 -0.04 -0.10 -0.01 -0.01 -0.04 -0.05 -0.06 -0.09 -0.13 0.05 -0.02
Mobiles & Tablets_Mobiles 0.19 -0.05 -0.01 0.00 -0.05 -0.01 -0.09 0.00 -0.06 -0.08 -0.05 0.09 -0.05 -0.02 -0.06 0.04 0.00 -0.06 -0.02 -0.02 -0.09 -0.03 -0.01 -0.04 -0.12 -0.10 0.01 0.01 0.00 -0.04 -0.60 0.00 -0.03 0.01 0.02 0.01 -0.12 -0.15 -0.09 -0.05
Fashion and Accessories_Belts 0.07 0.02 -0.02 -0.02 -0.02 -0.03 -0.03 0.04 -0.08 0.04 0.01 -0.04 -0.02 -0.02 0.01 -0.04 -0.10 0.01 -0.02 -0.02 0.06 -0.02 0.09 -0.02 0.05 0.07 0.02 -0.03 -0.03 -0.12 0.02 -0.09 0.03 0.06 -0.20 -0.09 0.17 -0.12 -0.07 0.13
Home Appliances_Garment Care 0.06 -0.06 -0.13 0.00 -0.02 0.58 -0.03 -0.06 0.05 -0.09 -0.08 0.00 0.01 -0.01 0.02 -0.01 -0.16 -0.12 0.03 -0.03 -0.02 -0.06 -0.04 0.00 -0.01 -0.04 -0.05 -0.05 -0.04 -0.02 0.08 -0.05 -0.01 -0.08 -0.04 -0.13 -0.19 -0.16 -0.01 -0.03
Travel & Luggage_Backpacks 0.04 0.01 -0.01 0.00 -0.02 -0.02 -0.01 -0.02 0.14 0.01 -0.08 -0.01 -0.06 0.05 -0.01 0.03 -0.13 -0.03 -0.04 -0.03 -0.05 -0.02 -0.04 0.00 -0.04 -0.06 -0.03 -0.04 -0.04 -0.05 0.09 -0.03 -0.03 -0.10 -0.02 -0.09 0.63 -0.02 -0.03 -0.10
Computers & Laptops_Network Components 0.04 -0.01 -0.01 -0.02 0.01 -0.01 -0.06 0.01 -0.01 -0.02 -0.01 -0.06 0.10 -0.01 0.17 0.35 -0.07 0.01 0.07 0.00 -0.01 -0.02 0.58 -0.02 -0.01 0.03 0.04 -0.03 -0.01 0.01 0.05 0.00 0.01 -0.07 0.08 -0.04 -0.08 0.00 0.04 0.00
Cameras_Instant Camera 0.04 -0.06 -0.02 -0.02 -0.04 -0.01 -0.04 0.08 -0.02 0.11 0.00 0.02 0.01 0.00 -0.03 -0.07 -0.11 0.05 -0.04 -0.02 -0.07 0.12 0.02 -0.01 0.03 -0.06 0.01 -0.08 -0.05 -0.22 0.05 -0.03 -0.11 -0.01 -0.11 -0.06 -0.11 -0.13 0.66 -0.08
Consumer Electronics_ 0.04 -0.03 0.00 0.01 -0.01 -0.03 0.00 0.15 0.01 -0.01 -0.01 0.06 -0.06 -0.03 -0.09 0.11 -0.12 -0.02 -0.03 -0.03 -0.05 0.57 -0.12 0.00 -0.02 -0.03 0.04 -0.01 0.00 -0.06 0.13 0.00 0.00 -0.02 -0.05 -0.08 -0.12 0.03 0.08 -0.02
Cameras_Camcorder 0.04 0.03 -0.03 -0.04 -0.02 -0.01 -0.03 0.02 0.00 0.01 -0.04 -0.04 -0.05 -0.01 0.02 -0.01 -0.05 0.01 0.04 0.01 -0.01 -0.09 0.05 -0.04 0.01 -0.02 -0.02 -0.04 -0.04 -0.04 0.10 0.00 0.00 0.65 -0.08 -0.08 0.03 -0.09 -0.10 -0.01
Sports_Automotives 0.03 -0.04 -0.03 0.03 -0.01 0.01 -0.01 -0.03 -0.13 0.02 0.02 -0.02 0.02 -0.01 0.02 0.00 -0.03 -0.01 -0.06 -0.06 0.75 -0.02 -0.02 -0.01 -0.07 -0.02 0.03 0.05 0.00 -0.05 0.09 -0.01 0.01 0.00 -0.08 -0.03 -0.01 0.00 -0.04 -0.02
Beauty & Health Care_Personal Care 0.03 -0.03 -0.02 0.01 -0.01 0.00 0.02 0.00 -0.04 0.02 0.01 0.00 0.04 0.01 0.00 -0.01 -0.06 0.01 0.00 0.73 -0.07 -0.03 -0.02 -0.01 0.00 0.00 0.03 0.03 -0.02 -0.04 0.06 -0.05 0.06 -0.02 -0.04 0.03 -0.02 -0.08 -0.07 -0.01
Home and Living_Kitchen Storage 0.03 -0.03 -0.01 0.00 0.01 -0.10 0.00 -0.06 0.01 -0.02 -0.03 -0.01 0.03 -0.01 0.04 0.01 -0.06 0.71 0.01 -0.01 -0.02 0.00 -0.02 -0.01 0.01 -0.05 -0.04 0.04 -0.04 0.02 0.03 -0.01 -0.02 -0.02 -0.01 -0.05 -0.06 -0.05 -0.04 -0.01
Sports_Leisure Sports and Games 0.03 -0.01 0.01 0.01 -0.01 0.01 -0.02 -0.01 -0.05 -0.01 0.02 0.01 0.02 0.00 -0.08 0.01 -0.05 -0.02 0.06 0.00 0.04 0.01 0.05 0.03 -0.03 0.03 0.06 -0.02 0.66 0.01 0.08 -0.01 0.04 -0.02 0.02 0.02 -0.06 -0.09 -0.02 -0.01
Beauty & Health Care_Men's Care 0.02 0.02 -0.01 0.02 0.01 0.02 0.00 0.01 -0.06 0.00 0.04 0.13 -0.04 -0.02 0.66 0.03 -0.03 0.00 0.10 0.00 0.03 0.06 0.10 0.00 -0.01 0.03 0.26 -0.03 -0.05 -0.02 -0.03 0.03 0.05 -0.05 0.06 -0.01 -0.02 -0.07 -0.01 -0.02
Books, Music & Movies_Movies 0.02 0.02 -0.03 0.01 0.00 0.07 0.00 -0.01 -0.02 0.73 -0.02 -0.05 -0.01 -0.01 0.00 0.01 -0.01 -0.02 0.09 0.04 -0.01 -0.02 -0.03 -0.03 -0.01 -0.03 0.00 -0.03 0.02 0.12 0.01 0.03 -0.02 -0.02 0.06 -0.03 0.04 0.00 0.03 0.06
Sports_ 0.02 0.02 0.00 0.62 -0.01 -0.01 -0.02 -0.01 -0.05 -0.01 0.04 0.12 -0.01 0.02 0.07 -0.01 -0.04 -0.02 -0.08 -0.04 0.03 -0.03 0.15 0.08 0.20 0.02 0.01 0.01 -0.04 0.17 0.02 0.00 0.01 0.07 -0.06 0.04 -0.02 0.06 0.00 -0.04
Fashion and Accessories_Women's Fashion 0.02 -0.04 -0.02 0.01 0.01 0.01 -0.02 0.00 -0.08 0.01 0.03 -0.01 0.05 -0.01 -0.01 0.02 -0.02 0.00 -0.01 -0.02 -0.08 -0.01 -0.03 -0.06 0.67 0.00 -0.02 -0.03 0.06 -0.06 0.08 0.11 0.15 0.00 -0.10 -0.06 -0.05 0.09 -0.06 -0.06
Cameras_Bridge (advanced Point & Shoot) 0.02 -0.01 0.00 0.03 -0.01 -0.02 0.04 0.00 0.00 0.01 0.00 0.00 -0.03 0.00 -0.06 -0.05 -0.06 -0.02 -0.02 -0.01 0.01 -0.04 0.68 0.01 -0.03 -0.02 -0.04 0.01 0.03 -0.01 -0.06 -0.01 0.02 0.02 -0.06 0.01 -0.01 -0.02 0.03 -0.01
Toys & Babies_Babies 0.01 -0.03 0.23 -0.04 -0.06 -0.05 -0.11 -0.01 -0.05 -0.13 -0.07 -0.09 -0.04 -0.05 0.04 -0.09 -0.20 -0.06 0.02 0.04 0.04 -0.18 -0.04 -0.11 -0.12 -0.07 -0.11 -0.03 -0.16 0.03 0.45 -0.10 -0.06 -0.08 -0.07 0.02 -0.24 -0.15 -0.19 -0.01
Home and Living_Home Decor 0.01 0.48 -0.02 -0.01 -0.02 0.06 0.05 -0.02 -0.13 0.00 0.32 0.01 -0.15 0.01 -0.03 -0.02 -0.03 0.07 0.06 -0.01 -0.07 -0.02 0.00 0.03 -0.01 -0.02 0.06 -0.01 -0.02 -0.08 0.09 0.05 -0.04 0.00 -0.03 -0.06 0.02 0.01 -0.10 -0.01
Beauty & Health Care_ 0.01 -0.08 -0.01 0.00 -0.01 0.04 0.30 0.08 -0.07 0.01 0.08 0.09 0.24 0.00 0.02 0.00 -0.04 0.00 -0.02 0.16 -0.04 0.07 -0.06 -0.02 0.18 -0.01 0.03 -0.05 -0.01 -0.07 0.00 -0.08 0.41 -0.04 0.04 -0.01 0.03 -0.16 -0.10 -0.01
Toys & Babies_ 0.01 -0.01 0.71 0.00 -0.02 -0.03 -0.02 0.01 0.01 -0.03 0.00 -0.01 -0.01 0.00 0.01 -0.01 -0.03 -0.06 0.02 -0.01 -0.02 -0.02 -0.01 -0.01 -0.02 -0.02 -0.03 0.01 -0.04 0.00 0.10 -0.01 -0.01 -0.03 0.01 -0.03 -0.11 -0.03 -0.04 0.00
Home Appliances_Cooling & Heating 0.01 -0.01 -0.06 0.00 0.00 0.47 0.03 0.45 0.03 -0.06 -0.04 0.04 -0.09 -0.02 -0.08 0.04 0.00 0.14 -0.03 -0.03 0.05 0.05 -0.01 -0.01 -0.08 -0.05 0.03 0.01 0.05 0.17 0.02 0.03 0.01 0.00 0.02 0.00 -0.05 0.11 -0.10 0.01
Fashion and Accessories_Other Accessories 0.01 0.00 0.00 -0.01 -0.01 -0.04 -0.03 0.01 0.70 -0.01 0.00 -0.04 0.02 0.05 0.09 -0.03 -0.04 -0.02 0.01 -0.01 0.01 -0.04 -0.01 -0.02 -0.02 -0.05 0.11 -0.02 -0.03 0.01 0.03 0.02 0.10 -0.01 -0.01 -0.03 0.14 0.00 -0.01 -0.02
Home and Living_Bed and Bath 0.01 0.10 -0.01 -0.01 0.00 0.06 -0.01 -0.03 0.07 0.01 -0.06 0.04 0.67 0.01 -0.02 0.01 -0.03 -0.01 -0.02 -0.02 -0.02 0.02 0.05 0.00 0.02 -0.03 0.00 0.03 0.01 0.01 0.02 0.02 -0.04 -0.03 0.00 -0.06 -0.08 0.04 -0.02 -0.01
Fashion and Accessories_Men's apparel 0.01 -0.02 -0.01 0.00 0.09 -0.02 -0.06 0.02 -0.02 -0.06 -0.02 -0.03 0.11 -0.01 -0.02 0.01 -0.05 0.00 0.03 0.04 0.06 -0.06 -0.04 -0.03 0.00 -0.04 0.06 0.66 0.06 0.04 0.00 -0.02 0.05 0.00 -0.01 -0.02 0.12 0.01 0.02 0.04
Home and Living_Sweet November Sale 0.01 0.24 -0.04 0.00 -0.03 0.00 -0.04 -0.02 -0.04 0.04 -0.02 -0.09 0.11 0.04 -0.02 -0.04 -0.05 0.03 -0.08 0.00 -0.03 -0.03 0.00 0.39 -0.11 -0.05 0.10 -0.06 0.03 -0.14 0.09 0.04 0.01 -0.06 -0.03 -0.16 -0.12 0.19 -0.08 -0.14
Home and Living_Stationery 0.01 0.09 -0.01 -0.01 0.00 0.01 -0.01 -0.03 -0.04 -0.01 0.73 0.01 -0.06 0.03 0.00 0.00 -0.04 -0.01 -0.01 -0.01 -0.08 0.01 0.00 -0.01 0.00 -0.03 -0.02 0.01 -0.02 0.01 0.02 0.02 -0.04 0.01 -0.01 -0.04 0.00 0.00 0.00 0.02
Consumer Electronics_Tv & Video 0.01 0.00 -0.04 -0.01 -0.01 0.04 0.00 0.00 -0.07 -0.04 -0.06 0.04 -0.05 0.01 -0.03 -0.03 0.02 -0.09 -0.01 0.00 -0.01 0.02 0.00 -0.04 0.01 0.62 0.00 -0.03 -0.02 0.03 0.09 -0.04 -0.04 -0.03 -0.05 -0.07 0.04 0.01 -0.06 0.04
Home and Living_Bath 0.01 0.07 0.00 0.07 -0.01 0.01 0.04 -0.04 0.00 0.03 -0.03 0.62 0.15 0.03 0.24 0.01 -0.02 -0.03 -0.06 -0.03 0.06 -0.04 -0.05 0.01 -0.01 0.00 -0.11 0.03 0.00 0.00 0.03 -0.03 -0.04 0.09 -0.04 -0.01 -0.03 0.15 0.02 -0.01
Fashion and Accessories_Women's shoes 0.01 0.01 0.01 0.00 0.83 0.01 0.01 0.00 -0.01 0.01 0.00 0.01 -0.02 0.00 0.00 0.00 0.00 -0.02 0.00 0.00 -0.01 -0.01 0.00 -0.01 -0.01 -0.01 -0.01 -0.05 -0.03 -0.01 0.02 -0.02 -0.02 0.00 0.00 0.01 -0.02 -0.03 -0.01 -0.02
Home and Living_Bathroom Accessories 0.01 -0.02 0.82 0.00 0.00 0.08 0.00 -0.01 -0.01 0.04 0.00 0.01 0.00 0.00 0.00 0.00 0.00 -0.04 0.01 -0.01 -0.01 -0.01 0.00 0.01 0.00 -0.01 0.01 0.00 -0.01 0.00 0.03 0.01 0.00 0.00 -0.01 -0.02 -0.03 0.01 -0.01 -0.01
Travel & Luggage_Travel and Luggage 0.01 0.01 0.00 -0.02 -0.01 -0.02 0.03 -0.02 0.02 -0.07 -0.02 -0.03 -0.01 0.00 0.02 0.01 -0.06 -0.01 -0.02 0.00 0.00 -0.01 -0.02 -0.01 0.02 0.03 0.00 -0.01 -0.04 0.10 -0.05 0.01 -0.03 -0.02 0.01 -0.08 -0.07 0.25 0.02 0.15
Fashion and Accessories_Women's apparel 0.00 -0.01 -0.01 0.11 0.04 -0.06 0.00 -0.01 0.18 0.03 -0.06 -0.04 0.03 0.07 -0.02 0.01 -0.05 0.04 0.02 -0.05 0.05 -0.04 0.00 -0.05 0.31 0.03 -0.04 0.00 -0.03 -0.06 0.06 0.58 0.18 -0.04 -0.08 -0.02 -0.01 -0.04 -0.06 -0.03
Beauty & Health Care_Food Supplements & Weight Management 0.00 -0.01 -0.01 -0.02 -0.02 -0.01 0.14 0.04 -0.19 0.15 0.05 -0.10 -0.06 0.00 0.21 0.02 -0.10 -0.03 -0.02 -0.06 0.04 -0.05 -0.07 0.01 -0.01 0.07 0.03 0.09 -0.07 -0.08 -0.05 0.06 0.05 -0.07 0.38 -0.03 0.07 0.03 0.00 -0.08
Books, Music & Movies_Music 0.00 -0.01 -0.05 -0.03 0.01 0.03 -0.01 0.00 0.01 0.16 -0.01 -0.04 0.04 -0.01 -0.02 0.03 0.00 0.02 0.01 0.03 -0.01 0.06 -0.04 -0.04 -0.07 -0.04 0.01 -0.04 0.05 0.53 -0.02 0.09 -0.03 -0.05 0.01 -0.05 -0.03 0.11 0.05 -0.05
Beauty & Health Care_Hair Care 0.00 0.10 0.00 -0.01 -0.02 -0.01 0.59 -0.02 0.08 -0.02 -0.07 0.05 0.04 -0.04 -0.07 -0.01 0.00 -0.02 0.23 -0.04 -0.06 -0.05 0.03 -0.02 -0.01 -0.04 0.15 0.14 -0.02 -0.01 0.06 0.05 -0.11 0.01 -0.05 0.06 -0.05 -0.02 0.00 0.06
Travel & Luggage_Travel bags and accessories 0.00 -0.01 0.00 0.00 -0.01 0.00 -0.03 0.00 0.09 0.00 -0.04 0.00 0.00 0.75 0.00 0.00 -0.03 0.00 0.20 0.00 0.07 0.02 0.00 -0.01 0.01 -0.01 -0.01 -0.04 -0.02 -0.02 0.00 0.05 0.02 -0.02 -0.02 0.00 0.07 0.01 -0.02 -0.04
Home and Living_Storage & Organisation 0.00 0.17 -0.02 -0.03 0.00 -0.02 0.20 -0.02 0.05 0.12 0.04 0.26 -0.12 -0.07 -0.03 0.01 -0.01 -0.02 0.58 -0.01 -0.01 -0.04 0.00 0.00 0.04 -0.04 0.08 0.04 -0.01 -0.01 0.06 0.03 -0.13 0.01 0.00 -0.03 0.03 -0.01 0.01 0.10
Home and Living_Bedding 0.00 0.20 -0.01 -0.02 -0.01 -0.03 -0.01 -0.02 -0.01 0.00 -0.04 0.26 0.43 -0.02 -0.09 -0.04 -0.02 -0.04 0.02 0.01 -0.06 -0.04 -0.01 0.00 -0.01 -0.03 0.05 0.05 0.02 -0.05 0.02 0.03 -0.09 -0.07 0.00 -0.06 -0.03 0.07 -0.03 -0.03
Books, Music & Movies_Books 0.00 0.01 -0.04 0.33 -0.02 0.05 0.03 -0.02 0.06 0.35 -0.04 -0.01 0.00 -0.05 -0.05 0.00 -0.01 0.03 0.15 0.01 -0.06 -0.04 -0.05 -0.04 -0.04 -0.06 -0.02 -0.01 0.01 -0.11 0.06 0.01 -0.06 -0.04 0.16 -0.07 0.00 -0.01 0.00 0.40
Consumer Electronics_Gaming 0.00 -0.02 -0.01 -0.01 -0.02 -0.03 -0.07 -0.01 0.06 -0.02 -0.03 0.01 0.02 0.01 -0.05 -0.04 -0.03 0.01 -0.01 -0.03 -0.08 -0.03 0.04 -0.04 0.04 -0.06 -0.04 -0.09 0.00 -0.07 0.07 -0.06 0.01 0.04 0.68 -0.03 -0.05 -0.07 -0.07 0.03
Home Appliances_Housekeeping 0.00 0.03 0.00 0.00 0.00 0.08 0.11 0.75 0.01 -0.03 0.00 0.00 -0.04 0.00 -0.01 0.02 0.00 0.01 -0.03 0.00 0.05 0.08 -0.01 -0.01 0.00 -0.02 -0.03 0.00 0.01 0.05 -0.01 -0.01 0.04 0.00 0.03 -0.01 -0.01 0.04 -0.10 0.01
Computers & Laptops_ 0.00 -0.01 0.00 0.00 0.00 0.01 0.02 -0.03 0.00 -0.06 -0.01 0.02 -0.02 0.00 -0.01 -0.01 0.03 -0.03 0.01 -0.01 0.00 -0.05 0.02 -0.01 -0.03 -0.01 -0.01 -0.01 -0.02 0.08 0.06 0.02 0.02 -0.03 0.00 0.01 0.05 0.04 0.47 0.03
Sports_Outdoor Sports 0.00 0.05 0.02 -0.03 -0.02 -0.01 -0.04 -0.01 0.02 -0.07 -0.06 -0.03 -0.08 -0.02 -0.01 -0.02 -0.03 -0.03 0.00 0.02 0.12 -0.01 -0.04 0.11 0.55 -0.01 0.05 0.06 -0.04 0.06 -0.06 -0.12 -0.23 -0.04 0.29 0.06 0.02 -0.03 0.06 0.04
Sports_Water Sports 0.00 -0.03 0.00 -0.01 0.04 0.00 0.02 0.00 0.05 0.02 -0.01 0.00 -0.01 -0.02 0.00 0.01 0.02 -0.01 0.02 0.01 0.00 0.04 0.01 0.70 -0.08 -0.01 -0.04 0.01 0.01 -0.09 0.04 0.02 0.04 -0.04 -0.02 0.00 0.01 0.01 -0.04 0.00
Books, Music & Movies_ 0.00 -0.02 0.01 -0.05 0.01 -0.03 -0.05 0.01 -0.04 -0.03 0.02 0.01 0.02 0.04 0.01 -0.01 -0.03 0.00 -0.05 -0.03 0.00 0.00 0.00 0.03 -0.03 0.03 0.03 -0.01 0.00 -0.04 0.01 0.03 0.07 -0.03 -0.05 0.01 -0.05 0.06 0.00 0.81
Travel & Luggage_Travel Accessories 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.01 -0.06 0.00 0.03 0.03 -0.01 0.78 -0.01 0.00 0.01 -0.01 -0.10 0.00 -0.05 -0.03 -0.01 0.00 -0.02 0.01 0.02 0.04 0.01 0.01 0.02 -0.03 -0.05 0.01 0.03 0.00 -0.04 -0.02 0.02 0.06
Cameras_Accessories -0.01 -0.03 0.01 0.03 0.02 0.00 0.00 -0.02 0.01 -0.02 -0.01 0.02 0.01 0.00 -0.02 0.02 0.00 -0.03 0.00 0.00 -0.01 0.07 -0.06 0.03 -0.03 -0.01 0.04 0.01 0.01 -0.01 -0.07 0.00 -0.01 0.70 0.07 0.03 -0.09 0.06 0.05 -0.03
Mobiles & Tablets_Mobile Accessories -0.01 -0.04 0.00 -0.02 -0.05 -0.09 -0.06 -0.01 -0.07 -0.01 -0.01 0.13 -0.05 -0.02 -0.09 0.58 -0.12 -0.04 -0.12 -0.01 0.02 0.10 -0.11 0.04 -0.10 -0.02 -0.05 -0.01 -0.03 -0.07 -0.01 0.02 0.00 0.09 0.07 0.06 0.12 0.05 -0.08 -0.03
Home Appliances_Large Home Appliances -0.01 0.03 0.31 0.00 0.00 0.51 -0.06 -0.04 -0.09 0.12 0.06 -0.01 0.01 -0.01 0.01 0.00 0.06 0.05 0.03 0.00 -0.01 -0.11 0.00 0.03 -0.03 0.07 0.07 -0.01 -0.02 -0.07 0.04 0.03 0.02 0.01 0.02 -0.05 0.02 0.18 0.02 -0.04
Beauty & Health Care_Bath & Body -0.01 -0.03 0.00 -0.01 -0.01 -0.02 0.60 0.10 -0.05 -0.01 0.00 0.00 -0.02 0.00 0.12 -0.04 0.00 -0.01 0.03 0.10 -0.04 -0.02 0.00 0.00 0.00 -0.02 -0.06 -0.06 -0.03 0.01 0.00 0.00 0.00 -0.01 -0.02 -0.04 -0.01 -0.06 -0.02 0.00
Toys & Babies_Kids -0.01 -0.03 0.07 -0.05 0.00 -0.05 -0.04 0.01 0.01 0.62 -0.03 0.06 -0.01 0.03 0.01 -0.02 -0.01 -0.03 -0.12 -0.03 0.06 0.00 0.03 0.02 0.00 0.03 -0.01 0.05 -0.03 0.10 0.01 -0.07 0.07 0.02 -0.07 0.05 -0.05 -0.03 -0.08 -0.08
Sports_Ball Sports -0.01 -0.04 0.00 0.01 -0.03 0.00 -0.02 0.01 -0.03 -0.06 -0.03 0.02 -0.01 0.00 0.01 0.00 0.00 0.00 0.03 -0.01 0.01 -0.06 -0.01 0.64 0.15 0.00 -0.02 -0.01 -0.03 0.17 -0.05 -0.05 -0.04 0.06 0.00 0.04 0.04 -0.11 0.05 0.07
Beauty & Health Care_Makeup -0.01 0.00 -0.01 0.01 0.00 -0.04 0.46 -0.11 0.01 -0.02 -0.07 -0.02 -0.11 0.02 -0.06 0.01 -0.08 0.00 -0.15 -0.07 0.02 -0.01 0.01 0.01 -0.06 0.06 -0.02 -0.11 0.04 -0.02 0.02 -0.03 0.04 -0.10 0.00 0.00 0.03 0.11 0.02 -0.09
Beauty & Health Care_Face -0.01 -0.06 0.01 0.00 0.00 -0.01 0.46 0.03 -0.06 -0.01 0.14 -0.12 0.35 0.03 0.01 -0.02 0.00 0.00 -0.06 -0.04 0.15 0.00 -0.03 -0.02 0.00 0.00 0.05 0.04 -0.03 0.03 -0.02 -0.03 0.09 0.14 0.09 0.01 0.00 -0.02 0.04 0.00
Beauty & Health Care_Personal Pleasure -0.01 -0.05 0.01 -0.01 -0.02 -0.03 0.02 0.03 0.00 -0.05 0.03 -0.10 0.36 -0.04 0.04 0.01 0.02 0.03 0.02 -0.01 0.03 -0.02 -0.04 0.04 -0.02 0.01 -0.06 -0.05 -0.07 0.07 0.03 -0.01 -0.02 0.03 -0.04 0.10 0.27 -0.16 0.02 0.14
Home and Living_Others -0.01 0.81 0.00 -0.01 0.00 0.00 -0.01 0.03 0.00 0.00 -0.01 0.02 0.07 -0.01 0.01 0.00 0.00 -0.02 0.03 0.00 0.00 0.00 0.00 -0.01 0.00 -0.01 -0.02 -0.02 -0.01 -0.02 0.01 -0.01 0.01 -0.01 0.01 0.00 -0.01 -0.02 0.01 0.00
Sports_Racket Sports -0.01 0.05 0.49 0.01 0.01 0.03 0.03 0.00 0.01 0.00 -0.01 0.01 0.00 0.00 -0.03 0.03 0.00 0.14 -0.05 0.01 0.01 0.07 0.01 0.00 0.02 0.00 0.01 -0.03 0.10 0.00 -0.15 0.01 0.00 0.02 -0.02 0.03 0.19 0.00 0.06 0.02
Beauty & Health Care_Fragrances -0.01 0.16 0.02 0.01 -0.01 -0.02 0.04 -0.06 0.01 -0.11 -0.07 -0.09 -0.12 0.01 0.06 0.01 0.00 -0.04 -0.02 0.08 0.06 -0.06 -0.01 -0.02 -0.03 0.02 -0.11 0.16 0.00 0.18 0.00 -0.10 0.41 0.03 0.11 -0.03 -0.06 0.14 0.34 0.07
Fashion and Accessories_Jewelry -0.01 -0.06 0.00 0.02 -0.01 -0.05 -0.03 0.02 0.17 -0.04 0.59 0.02 0.03 -0.03 0.00 -0.01 0.01 -0.03 0.00 0.00 0.11 -0.02 -0.01 -0.04 0.00 -0.07 -0.03 -0.04 0.04 0.05 0.02 -0.05 0.00 -0.06 -0.01 0.01 -0.05 0.01 -0.01 0.00
Home and Living_ -0.01 -0.07 -0.01 0.01 -0.03 0.04 -0.09 0.05 -0.15 -0.07 0.04 0.07 0.02 -0.05 -0.03 0.00 0.01 -0.06 0.26 0.00 -0.04 -0.22 -0.04 0.01 -0.09 0.06 0.05 0.22 0.02 0.02 0.06 0.10 0.08 -0.01 0.04 -0.03 0.29 0.04 0.11 -0.11
Fashion and Accessories_Men's Fashion -0.01 -0.01 -0.01 -0.01 -0.07 -0.03 0.03 0.00 0.06 0.10 0.00 0.00 -0.07 0.03 0.02 -0.02 -0.01 0.04 -0.04 -0.04 -0.06 0.07 0.04 0.02 0.02 0.01 -0.07 0.65 -0.08 -0.11 0.02 0.00 -0.03 -0.03 -0.05 0.01 -0.19 -0.06 -0.08 -0.06
Sports_Exercise and Fitness -0.01 -0.01 0.00 0.75 0.00 0.00 0.00 0.00 0.00 0.03 -0.01 -0.02 -0.01 0.00 -0.01 -0.01 0.01 0.00 0.01 0.01 0.01 0.00 -0.02 -0.02 -0.02 -0.01 0.01 -0.01 0.01 -0.04 0.01 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.02
Books, Music & Movies_TV Series -0.01 0.00 0.01 0.64 0.00 -0.01 -0.01 0.01 0.02 -0.04 -0.01 -0.04 0.00 -0.01 -0.02 0.00 0.00 0.01 0.02 0.02 -0.02 0.02 -0.04 -0.02 -0.08 0.00 -0.01 -0.01 0.01 -0.05 -0.03 0.00 -0.02 -0.06 0.01 -0.01 0.01 -0.05 -0.01 -0.02
Home Appliances_Personal Care & Health -0.01 0.00 0.00 -0.01 -0.01 -0.01 -0.02 -0.01 0.15 -0.04 -0.08 0.06 -0.08 0.01 -0.13 -0.03 0.03 -0.03 -0.09 0.01 -0.09 0.01 -0.01 -0.04 0.03 -0.07 0.60 -0.01 0.03 0.03 0.05 -0.05 -0.01 0.01 -0.13 -0.05 -0.02 0.03 -0.02 0.05
Computers & Laptops_Tablet Accessories -0.01 0.00 0.01 -0.01 0.00 0.01 0.00 -0.01 0.01 0.00 0.01 -0.05 0.02 0.02 0.02 0.69 0.05 0.01 0.04 0.01 -0.02 -0.03 0.14 0.00 0.05 0.00 0.02 0.00 -0.01 0.00 0.03 -0.01 -0.01 -0.02 -0.04 0.00 -0.03 -0.03 -0.01 0.00
Travel & Luggage_Travel Bags and Accessories -0.01 -0.05 0.01 0.01 0.00 -0.01 -0.05 -0.01 -0.01 -0.06 -0.04 -0.10 0.04 0.14 0.02 -0.02 -0.01 0.02 0.60 -0.02 0.03 0.13 0.00 0.03 -0.02 0.01 -0.11 -0.04 0.04 0.01 -0.06 -0.06 0.06 0.03 -0.02 0.01 -0.05 0.06 -0.03 -0.07
Travel & Luggage_Everyday Bags -0.01 -0.01 0.01 -0.04 -0.03 0.00 -0.01 0.00 -0.07 -0.05 0.01 -0.01 0.01 -0.02 0.04 -0.03 0.00 -0.03 -0.04 0.03 0.00 0.00 0.00 0.02 -0.09 -0.02 -0.02 -0.02 -0.02 0.06 -0.02 0.77 -0.08 0.02 0.01 0.03 -0.01 -0.03 0.03 0.04
Beauty & Health Care_Gift Sets -0.01 0.00 -0.01 -0.01 -0.01 0.01 0.04 0.01 0.18 -0.01 -0.06 0.10 -0.04 0.01 0.65 -0.03 0.04 0.01 -0.10 0.01 -0.08 -0.01 -0.08 0.00 0.00 -0.09 -0.15 0.01 0.14 -0.02 0.07 0.00 -0.09 0.05 -0.06 -0.01 0.01 0.09 -0.02 0.03
Fashion and Accessories_Apparel & Shoes -0.01 0.02 -0.01 -0.02 -0.02 0.00 -0.05 -0.02 0.09 0.05 -0.05 0.06 -0.10 -0.04 -0.04 -0.03 0.02 -0.01 0.00 -0.05 -0.03 0.02 0.04 0.03 0.00 -0.07 0.01 0.01 -0.01 -0.05 0.01 0.05 0.65 0.00 -0.02 0.01 -0.01 -0.02 -0.03 0.04
Toys & Babies_Toys -0.02 -0.01 0.06 0.03 -0.05 -0.09 -0.02 0.00 -0.02 0.09 0.08 0.05 -0.03 0.00 -0.02 -0.10 -0.07 0.00 0.00 -0.06 -0.10 -0.06 0.05 0.06 0.06 0.01 0.01 -0.03 -0.09 0.61 0.10 -0.08 -0.02 0.00 -0.13 0.01 0.00 -0.14 -0.07 -0.01
Fashion and Accessories_Shoes -0.02 -0.02 -0.01 -0.02 0.85 -0.03 -0.03 -0.01 0.00 -0.01 -0.02 -0.01 0.01 -0.01 0.00 -0.03 -0.03 0.02 -0.01 -0.01 -0.01 -0.01 -0.01 0.02 0.01 0.01 0.00 0.08 0.01 -0.02 0.02 0.02 0.00 0.00 -0.02 -0.01 0.00 0.00 -0.02 0.02
Home and Living_Kitchen & Dining -0.02 0.72 0.02 0.02 0.00 0.04 -0.01 0.02 0.04 0.00 -0.05 0.07 0.17 0.00 0.03 0.00 -0.02 0.00 -0.02 -0.01 0.05 0.01 -0.02 -0.03 0.01 0.02 -0.03 0.00 -0.01 0.05 -0.04 -0.03 0.05 0.01 -0.02 0.10 0.00 -0.04 0.01 -0.01
Fashion and Accessories_ -0.02 -0.05 0.00 -0.01 0.00 0.08 0.02 -0.01 0.56 0.03 0.34 -0.04 0.07 -0.05 -0.03 0.01 -0.01 0.00 0.01 -0.01 0.01 0.00 0.00 0.07 -0.06 0.27 -0.03 0.11 -0.03 -0.04 -0.03 0.00 -0.01 0.05 0.05 0.06 -0.08 -0.03 0.00 -0.02
Beauty & Health Care_Health & Beauty Tools -0.02 -0.02 0.00 0.02 -0.01 -0.02 0.06 -0.02 -0.05 0.02 0.02 -0.10 0.09 0.01 0.21 0.00 -0.03 0.01 0.03 0.00 0.09 -0.01 -0.02 0.00 -0.02 0.05 0.68 0.01 -0.04 0.00 -0.03 0.00 0.00 0.02 0.08 0.07 0.00 -0.03 -0.01 -0.02
Home Appliances_ -0.02 0.04 0.02 -0.01 -0.02 0.15 -0.02 0.05 -0.02 -0.01 0.00 0.03 -0.05 0.00 -0.03 -0.02 0.04 0.74 -0.01 0.01 0.02 -0.03 0.01 0.00 -0.02 0.05 0.03 -0.02 0.02 -0.01 0.00 -0.01 0.00 0.00 0.00 0.03 0.04 0.04 0.03 0.01
Computers & Laptops_Printers & Ink -0.02 0.02 0.01 0.00 0.00 -0.02 0.00 -0.01 0.03 -0.01 -0.01 -0.01 -0.05 -0.01 0.00 0.01 0.02 -0.01 -0.02 0.72 0.07 0.01 0.02 0.02 -0.02 0.01 -0.02 -0.02 0.00 0.02 -0.04 0.05 -0.06 0.02 0.01 -0.03 -0.01 0.08 0.05 -0.02
Home and Living_Home Improvement -0.02 0.07 0.01 -0.04 -0.01 -0.03 -0.02 0.05 0.26 0.03 0.00 0.19 -0.05 0.03 -0.09 -0.02 0.02 0.02 0.11 0.10 0.61 0.00 0.02 0.00 0.10 -0.04 -0.03 -0.07 0.00 -0.05 -0.03 0.06 -0.07 -0.04 0.03 -0.02 -0.04 -0.03 0.01 0.00
Consumer Electronics_Gadget and Gizmos -0.02 0.02 0.01 0.00 -0.01 -0.04 -0.10 -0.07 -0.08 -0.03 0.00 -0.11 0.10 0.01 0.14 -0.08 0.03 0.00 0.24 -0.01 0.01 0.57 0.00 -0.03 -0.05 0.06 0.01 -0.06 -0.04 0.12 -0.04 -0.02 0.10 0.00 0.02 0.00 0.01 -0.06 -0.06 -0.04
Cameras_SLR -0.02 0.01 0.02 0.00 -0.01 -0.13 -0.10 0.63 -0.01 0.05 0.00 -0.05 0.05 0.01 0.06 -0.05 0.00 -0.05 0.03 0.00 -0.05 -0.08 0.03 0.01 0.02 0.04 0.00 0.01 -0.04 -0.10 -0.01 -0.01 -0.06 0.00 -0.04 0.02 0.02 -0.07 0.14 -0.01
Fashion and Accessories_Bags -0.02 0.00 0.01 0.00 0.00 -0.02 -0.01 0.01 0.12 0.03 -0.02 -0.01 0.00 -0.01 0.00 0.00 0.01 0.09 0.00 0.01 -0.03 -0.03 -0.01 0.00 0.00 0.72 -0.01 -0.01 0.03 -0.05 -0.04 0.03 -0.02 0.00 0.01 0.04 -0.06 -0.01 0.01 -0.02
Home and Living_Storage Containers and Organizers -0.03 0.02 0.01 -0.04 0.01 -0.02 -0.05 0.00 -0.04 -0.02 0.05 0.65 -0.01 0.01 0.03 -0.01 0.00 0.04 0.08 0.01 0.06 0.00 0.02 -0.01 -0.01 0.04 0.04 -0.05 -0.04 0.00 -0.06 -0.02 0.11 -0.08 0.01 0.02 0.01 -0.14 0.01 0.00
Cameras_Other Cameras -0.03 0.00 0.02 -0.01 -0.02 -0.04 -0.01 0.01 0.01 0.00 -0.05 -0.07 -0.02 -0.02 0.03 -0.09 -0.12 -0.01 0.04 0.00 0.00 -0.10 0.04 -0.03 0.01 -0.01 -0.06 -0.04 -0.13 -0.03 -0.50 -0.05 -0.01 -0.08 -0.12 -0.10 -0.07 0.14 -0.15 0.03
Fashion and Accessories_Watch -0.04 -0.02 0.01 -0.02 -0.01 -0.06 -0.01 0.01 0.02 0.00 0.00 -0.05 -0.03 -0.01 0.15 -0.05 0.00 0.00 -0.04 -0.01 -0.04 -0.05 -0.03 -0.04 0.06 -0.02 -0.08 0.01 0.71 -0.03 -0.03 -0.03 -0.05 -0.01 -0.05 -0.02 0.02 0.01 -0.03 0.00
Home Appliances_Small Kitchen Appliances -0.04 0.10 0.07 -0.02 -0.01 0.67 -0.03 0.01 0.00 0.03 0.00 -0.02 0.06 0.03 0.03 -0.05 0.00 0.06 -0.03 0.01 -0.01 0.07 0.01 -0.02 0.05 0.04 -0.05 -0.01 -0.03 -0.03 -0.07 -0.03 0.00 0.03 -0.02 0.14 0.10 -0.06 0.04 0.01
Travel & Luggage_ -0.05 0.04 -0.03 0.01 -0.01 0.01 0.00 0.01 0.00 0.01 -0.03 -0.01 -0.05 0.00 0.00 0.00 -0.05 -0.04 -0.01 -0.04 -0.01 -0.03 0.01 -0.02 -0.05 -0.02 0.01 -0.04 -0.01 -0.04 0.07 0.02 0.02 -0.03 -0.04 0.76 -0.07 0.02 -0.02 -0.02
Consumer Electronics_Audio -0.07 0.00 0.00 -0.03 -0.02 0.15 0.03 -0.12 0.01 -0.01 -0.02 -0.03 -0.07 -0.02 0.06 -0.12 0.11 -0.08 -0.10 0.04 0.02 0.42 0.20 0.02 0.09 -0.05 -0.09 0.18 -0.01 -0.06 -0.02 -0.01 -0.16 -0.01 -0.07 0.08 0.23 -0.01 -0.08 0.10
Travel & Luggage_Luggage -0.07 -0.09 -0.02 -0.02 -0.03 -0.04 -0.07 0.02 -0.07 0.05 0.08 -0.05 0.08 -0.02 0.02 -0.08 0.02 0.01 0.15 -0.01 -0.05 -0.12 -0.01 -0.03 0.08 -0.06 -0.02 -0.09 -0.08 -0.18 0.02 -0.11 0.03 -0.03 -0.15 0.20 0.01 0.54 -0.01 -0.06
Consumer Electronics_TV & Video -0.10 -0.03 -0.02 0.00 -0.02 0.03 -0.03 0.06 -0.03 -0.04 -0.03 0.13 -0.02 0.00 -0.10 -0.18 0.40 0.04 -0.09 0.03 -0.02 0.08 0.36 -0.04 -0.03 -0.05 -0.01 0.00 -0.03 -0.01 0.14 0.02 -0.03 0.02 0.23 -0.01 0.09 0.14 -0.12 -0.02
Mobiles & Tablets_Tablet Accessories -0.11 0.02 -0.01 0.00 0.00 0.03 0.01 0.00 0.02 0.00 -0.04 -0.08 -0.02 0.00 0.05 0.39 0.36 -0.02 0.03 0.00 0.00 -0.09 -0.08 -0.05 0.08 -0.07 -0.04 0.00 -0.03 0.01 -0.06 -0.09 -0.04 -0.04 -0.15 -0.06 -0.04 -0.06 0.01 0.03

To better visualize and interpret the factors we will “supress” loadings with small values. However, as the data is much less clean than in other examples, we will keep it with absolute values higher than 0.2. In this case our factors look as follows after suppressing the small numbers:

Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9 Comp.10 Comp.11 Comp.12 Comp.13 Comp.14 Comp.15 Comp.16 Comp.17 Comp.18 Comp.19 Comp.20 Comp.21 Comp.22 Comp.23 Comp.24 Comp.25 Comp.26 Comp.27 Comp.28 Comp.29 Comp.30 Comp.31 Comp.32 Comp.33 Comp.34 Comp.35 Comp.36 Comp.37 Comp.38 Comp.39 Comp.40
Cameras_DSLR/SLR 0.82
Computers & Laptops_Laptops 0.66
Cameras_Point & Shoot (plain digital) 0.64
Computers & Laptops_Computer Accessories 0.40 0.42
Mobiles & Tablets_Landline Phones 0.40 0.47
Computers & Laptops_Tablets 0.23 0.56
Mobiles & Tablets_Tablets 0.21 0.58
Mobiles & Tablets_Mobiles -0.60
Fashion and Accessories_Belts -0.20
Home Appliances_Garment Care 0.58
Travel & Luggage_Backpacks 0.63
Computers & Laptops_Network Components 0.35 0.58
Cameras_Instant Camera -0.22 0.66
Consumer Electronics_ 0.57
Cameras_Camcorder 0.65
Sports_Automotives 0.75
Beauty & Health Care_Personal Care 0.73
Home and Living_Kitchen Storage 0.71
Sports_Leisure Sports and Games 0.66
Beauty & Health Care_Men's Care 0.66 0.26
Books, Music & Movies_Movies 0.73
Sports_ 0.62 0.20
Fashion and Accessories_Women's Fashion 0.67
Cameras_Bridge (advanced Point & Shoot) 0.68
Toys & Babies_Babies 0.23 -0.20 0.45 -0.24
Home and Living_Home Decor 0.48 0.32
Beauty & Health Care_ 0.30 0.24 0.41
Toys & Babies_ 0.71
Home Appliances_Cooling & Heating 0.47 0.45
Fashion and Accessories_Other Accessories 0.70
Home and Living_Bed and Bath 0.67
Fashion and Accessories_Men's apparel 0.66
Home and Living_Sweet November Sale 0.24 0.39
Home and Living_Stationery 0.73
Consumer Electronics_Tv & Video 0.62
Home and Living_Bath 0.62 0.24
Fashion and Accessories_Women's shoes 0.83
Home and Living_Bathroom Accessories 0.82
Travel & Luggage_Travel and Luggage 0.25
Fashion and Accessories_Women's apparel 0.31 0.58
Beauty & Health Care_Food Supplements & Weight Management 0.21 0.38
Books, Music & Movies_Music 0.53
Beauty & Health Care_Hair Care 0.59 0.23
Travel & Luggage_Travel bags and accessories 0.75 0.20
Home and Living_Storage & Organisation 0.20 0.26 0.58
Home and Living_Bedding 0.20 0.26 0.43
Books, Music & Movies_Books 0.33 0.35 0.40
Consumer Electronics_Gaming 0.68
Home Appliances_Housekeeping 0.75
Computers & Laptops_ 0.47
Sports_Outdoor Sports 0.55 -0.23 0.29
Sports_Water Sports 0.70
Books, Music & Movies_ 0.81
Travel & Luggage_Travel Accessories 0.78
Cameras_Accessories 0.70
Mobiles & Tablets_Mobile Accessories 0.58
Home Appliances_Large Home Appliances 0.31 0.51
Beauty & Health Care_Bath & Body 0.60
Toys & Babies_Kids 0.62
Sports_Ball Sports 0.64
Beauty & Health Care_Makeup 0.46
Beauty & Health Care_Face 0.46 0.35
Beauty & Health Care_Personal Pleasure 0.36 0.27
Home and Living_Others 0.81
Sports_Racket Sports 0.49
Beauty & Health Care_Fragrances 0.41 0.34
Fashion and Accessories_Jewelry 0.59
Home and Living_ 0.26 -0.22 0.22 0.29
Fashion and Accessories_Men's Fashion 0.65
Sports_Exercise and Fitness 0.75
Books, Music & Movies_TV Series 0.64
Home Appliances_Personal Care & Health 0.60
Computers & Laptops_Tablet Accessories 0.69
Travel & Luggage_Travel Bags and Accessories 0.60
Travel & Luggage_Everyday Bags 0.77
Beauty & Health Care_Gift Sets 0.65
Fashion and Accessories_Apparel & Shoes 0.65
Toys & Babies_Toys 0.61
Fashion and Accessories_Shoes 0.85
Home and Living_Kitchen & Dining 0.72
Fashion and Accessories_ 0.56 0.34 0.27
Beauty & Health Care_Health & Beauty Tools 0.21 0.68
Home Appliances_ 0.74
Computers & Laptops_Printers & Ink 0.72
Home and Living_Home Improvement 0.26 0.61
Consumer Electronics_Gadget and Gizmos 0.24 0.57
Cameras_SLR 0.63
Fashion and Accessories_Bags 0.72
Home and Living_Storage Containers and Organizers 0.65
Cameras_Other Cameras -0.50
Fashion and Accessories_Watch 0.71
Home Appliances_Small Kitchen Appliances 0.67
Travel & Luggage_ 0.76
Consumer Electronics_Audio 0.42 0.20 0.23
Travel & Luggage_Luggage 0.20 0.54
Consumer Electronics_TV & Video 0.40 0.36 0.23
Mobiles & Tablets_Tablet Accessories 0.39 0.36

Customer clustering

From the Factor Analysis performed above, we have extracted the 40 most important factors. For the sake of simplicity, each factor will be represented buy the subcategory the most important.

We’ll use the Kmeans method to cluster our customers.

The histogram of all pairwise distances for the euclidean distance:

Hierarchial Clustering

Let’s fist use the Hierarchial Clustering method, as we do not know for now how many segments there are in our data.It should produce a Dendrogram, but here the data seems to be too massive to be represented in such a format…

We will visualise now the distance with respect to the number of clusters to be able to choose the appropriate number of clusters.

Let’s zoom in to see the elbow of the plot..

As a rule of thumb, one can select the number of clusters as the “elbow” of this plot: this is the place in the tree where, if we traverse the tree from the leaves to its root, we need to make the “longest jump” before we merge further the segments at that tree level.

For now let’s consider the 10-segments solution found by the Hierarchical Clustering method (using the euclidean distance and the hclust option ward.D). We can also see the segment each observation (respondent in this case) belongs to for the first 10 people:

Observation Number Cluster_Membership
1 1
2 2
3 2
4 2
5 2
6 2
7 3
8 2
9 4
10 2

Let’s now try to observe these clusters. At first tlet’s visualize how many customers are in each cluster.

The Cluster number 2 seems to be the one with the most of customers.

Let’s try to visualize the definition of each cluster.

Cameras_DSLR/SLR Home and Living_Others Home and Living_Bathroom Accessories Sports_Exercise and Fitness Fashion and Accessories_Shoes Home Appliances_Small Kitchen Appliances Beauty & Health Care_Bath & Body Home Appliances_Housekeeping Fashion and Accessories_Other Accessories Books, Music & Movies_Movies Home and Living_Stationery Home and Living_Storage Containers and Organizers Home and Living_Bed and Bath Travel & Luggage_Travel Accessories Beauty & Health Care_Men's Care Computers & Laptops_Tablet Accessories Mobiles & Tablets_Tablets Home Appliances_ Travel & Luggage_Travel Bags and Accessories Beauty & Health Care_Personal Care Sports_Automotives Consumer Electronics_ Cameras_Bridge (advanced Point & Shoot) Sports_Water Sports Fashion and Accessories_Women's Fashion Fashion and Accessories_Bags Beauty & Health Care_Health & Beauty Tools Fashion and Accessories_Men's apparel Fashion and Accessories_Watch Toys & Babies_Toys Toys & Babies_Babies Travel & Luggage_Everyday Bags Fashion and Accessories_Apparel & Shoes Cameras_Accessories Consumer Electronics_Gaming Travel & Luggage_ Travel & Luggage_Backpacks Travel & Luggage_Luggage Cameras_Instant Camera Books, Music & Movies_
0.01216216 0.122972973 0.000000000 0.02702703 0.03918919 0.11216216 0.016216216 0.017567568 0.162162162 0.062162162 0.07162162 0.028378378 0.039189189 0.001351351 0.022972973 0.008108108 0.02702703 0.05000000 0.005405405 0.01081081 0.036486486 0.032432432 0.005405405 0.004054054 0.03513514 0.024324324 0.148648649 0.055405405 0.017567568 0.035135135 0.02972973 0.001351351 0.004054054 0.064864865 0.05000000 0.020270270 0.23918919 0.063513514 0.151351351 0.002702703
0.00000000 0.000000000 0.000000000 0.00000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000
0.03623188 0.000000000 0.000000000 0.00000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.76449275 0.00000000 0.000000000 0.00000000 0.007246377 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.003623188 0.000000000 0.003623188 0.000000000 0.02898551 0.000000000 0.000000000 0.014492754 0.00000000 0.000000000 0.00000000 0.000000000 0.021739130 0.000000000
0.00000000 0.000000000 0.000000000 0.00000000 0.07352941 0.01470588 0.044117647 0.000000000 0.029411765 0.007352941 0.00000000 0.000000000 0.000000000 0.000000000 0.007352941 0.000000000 0.06617647 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.007352941 0.000000000 0.029411765 1.742647059 0.000000000 0.00000000 0.000000000 0.000000000 0.022058824 0.00000000 0.000000000 0.05882353 0.007352941 0.014705882 0.000000000
0.00000000 0.000000000 0.000000000 0.00000000 0.03870968 0.01935484 0.051612903 0.000000000 0.006451613 0.006451613 0.00000000 0.006451613 0.000000000 0.000000000 0.000000000 0.000000000 0.02580645 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.012903226 0.000000000 0.032258065 2.109677419 0.13548387 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.04516129 0.000000000 0.000000000 0.000000000
0.00000000 0.003690037 0.000000000 0.00000000 0.06642066 0.04059041 0.003690037 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.01845018 0.00000000 0.000000000 0.00000000 0.003690037 0.003690037 0.000000000 0.000000000 0.00000000 0.000000000 0.003690037 0.000000000 0.018450185 0.077490775 1.60516605 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.03321033 0.000000000 0.011070111 0.000000000
0.00000000 0.032432432 0.000000000 0.00000000 0.01081081 1.67027027 0.005405405 0.005405405 0.000000000 0.000000000 0.00000000 0.005405405 0.005405405 0.000000000 0.000000000 0.000000000 0.01081081 0.02162162 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.016216216 0.00000000 0.005405405 0.02162162 0.005405405 0.000000000 0.000000000
0.00000000 0.000000000 0.000000000 0.00800000 0.03200000 0.02400000 2.392000000 0.016000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.016000000 0.000000000 0.04000000 0.00000000 0.000000000 0.02400000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.032000000 0.000000000 0.000000000 0.016000000 0.08800000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.01600000 0.000000000 0.008000000 0.000000000
0.00000000 0.036144578 0.006024096 0.00000000 0.01807229 0.07831325 0.030120482 0.000000000 0.006024096 0.000000000 0.01204819 0.000000000 0.006024096 0.000000000 0.000000000 0.000000000 0.01204819 0.00000000 0.000000000 0.01204819 0.036144578 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.018072289 0.006024096 0.000000000 0.108433735 4.63855422 0.000000000 0.000000000 0.000000000 0.01204819 0.012048193 0.03012048 0.012048193 0.006024096 0.000000000
0.00000000 0.011111111 0.000000000 0.00000000 1.88333333 0.01111111 0.011111111 0.000000000 0.005555556 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.005555556 0.000000000 0.02777778 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000 0.000000000 0.005555556 0.02222222 0.005555556 0.005555556 0.061111111 0.027777778 0.005555556 0.01111111 0.000000000 0.000000000 0.005555556 0.00000000 0.000000000 0.03333333 0.000000000 0.000000000 0.000000000

Using Kmean Clustering

Here are the clusters our observations belong to when we select 10 clusters and the Lloyd kmeans method, for the first 10 people (note that the cluster IDs may differ from those from hierarchical clustering):

Observation Number Cluster_Membership
Customer5 1 2
Customer7 2 2
Customer9 3 2
Customer10 4 2
Customer14 5 2
Customer17 6 2
Customer19 7 2
Customer21 8 2
Customer23 9 2
Customer24 10 2

Let’s visualize the data, just like for the previous method.

The result here is widely differentm with a cluster 5 that has much more customers than the other ones. Let’s visualize the attributes of the clusters by computing the purchases of the “mean customer” for each cluster.

Cameras_DSLR/SLR Home and Living_Others Home and Living_Bathroom Accessories Sports_Exercise and Fitness Fashion and Accessories_Shoes Home Appliances_Small Kitchen Appliances Beauty & Health Care_Bath & Body Home Appliances_Housekeeping Fashion and Accessories_Other Accessories Books, Music & Movies_Movies Home and Living_Stationery Home and Living_Storage Containers and Organizers Home and Living_Bed and Bath Travel & Luggage_Travel Accessories Beauty & Health Care_Men's Care Computers & Laptops_Tablet Accessories Mobiles & Tablets_Tablets Home Appliances_ Travel & Luggage_Travel Bags and Accessories Beauty & Health Care_Personal Care Sports_Automotives Consumer Electronics_ Cameras_Bridge (advanced Point & Shoot) Sports_Water Sports Fashion and Accessories_Women's Fashion Fashion and Accessories_Bags Beauty & Health Care_Health & Beauty Tools Fashion and Accessories_Men's apparel Fashion and Accessories_Watch Toys & Babies_Toys Toys & Babies_Babies Travel & Luggage_Everyday Bags Fashion and Accessories_Apparel & Shoes Cameras_Accessories Consumer Electronics_Gaming Travel & Luggage_ Travel & Luggage_Backpacks Travel & Luggage_Luggage Cameras_Instant Camera Books, Music & Movies_
0.004504505 0.009009009 0.000000000 0.000000000 1.71621622 0.009009009 0.02252252 0.000000000 0.04954955 0.00000000 0.000000000 0.000000000 0.009009009 0.0000000000 0.004504505 0.00000000 0.03153153 0.004504505 0.000000000 0.000000000 0.009009009 0.000000000 0.000000000 0.004504505 0.018018018 0.004504505 0.022522523 0.018018018 0.067567568 0.03153153 0.05855856 0.0000000000 0.000000000 0.009009009 0.000000000 0.000000000 0.02702703 0.000000000 0.000000000 0.0045045045
0.003206413 0.031663327 0.000000000 0.008016032 0.00000000 0.000000000 0.02244489 0.003206413 0.04048096 0.01202405 0.000000000 0.007615230 0.008016032 0.0004008016 0.004809619 0.00240481 0.14589178 0.009218437 0.001603206 0.003607214 0.010420842 0.007615230 0.001603206 0.001202405 0.009619238 0.006412826 0.040080160 0.007214429 0.093787575 0.02965932 0.04729459 0.0004008016 0.001202405 0.021242485 0.014028056 0.004809619 0.07134269 0.018036072 0.000000000 0.0004008016
0.000000000 0.000000000 0.000000000 0.000000000 0.58823529 0.058823529 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.058823529 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.058823529 1.941176471 0.058823529 0.05882353 0.05882353 0.0000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.05882353 0.000000000 0.000000000 0.0000000000
0.000000000 0.011627907 0.000000000 0.000000000 0.00000000 0.116279070 0.01162791 0.000000000 0.00000000 0.04651163 0.011627907 0.000000000 0.000000000 0.0000000000 0.000000000 0.00000000 0.10465116 0.034883721 0.000000000 0.000000000 0.000000000 0.058139535 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.023255814 0.00000000 0.03488372 0.0000000000 0.000000000 0.011627907 0.000000000 0.000000000 0.04651163 0.011627907 1.430232558 0.0000000000
0.294117647 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000 0.000000000 0.00000000 4.02941176 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.147058824 0.00000000 0.00000000 0.0000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.0000000000
0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.03571429 0.000000000 0.00000000 0.00000000 1.750000000 0.035714286 0.000000000 0.0000000000 0.035714286 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.035714286 0.000000000 0.000000000 0.14285714 0.07142857 0.0000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.035714286 0.000000000 0.0000000000
0.000000000 0.021276596 0.003039514 0.000000000 0.02127660 0.045592705 0.01519757 0.000000000 0.01215805 0.00000000 0.006079027 0.000000000 0.003039514 0.0000000000 0.000000000 0.00000000 0.02127660 0.000000000 0.000000000 0.006079027 0.018237082 0.003039514 0.000000000 0.000000000 0.000000000 0.000000000 0.009118541 0.003039514 0.009118541 0.08814590 3.33130699 0.0000000000 0.000000000 0.000000000 0.006079027 0.006079027 0.02127660 0.006079027 0.003039514 0.0000000000
0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.031914894 0.02127660 0.000000000 0.06382979 0.07446809 0.010638298 0.010638298 0.000000000 0.0000000000 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.021276596 0.000000000 0.000000000 2.84042553 0.21276596 0.0000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.00000000 0.000000000 0.000000000 0.0000000000
0.000000000 0.053435115 0.000000000 0.000000000 0.04580153 1.503816794 0.01145038 0.022900763 0.01908397 0.02671756 0.007633588 0.007633588 0.030534351 0.0000000000 0.007633588 0.00000000 0.04580153 0.053435115 0.000000000 0.000000000 0.003816794 0.000000000 0.000000000 0.000000000 0.007633588 0.011450382 0.019083969 0.003816794 0.015267176 0.02290076 0.04198473 0.0000000000 0.000000000 0.011450382 0.007633588 0.015267176 0.06870229 0.007633588 0.000000000 0.0000000000
0.000000000 0.037974684 0.000000000 0.012658228 0.01265823 0.012658228 3.30379747 0.025316456 0.00000000 0.00000000 0.000000000 0.000000000 0.000000000 0.0000000000 0.063291139 0.00000000 0.03797468 0.000000000 0.000000000 0.025316456 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.063291139 0.000000000 0.025316456 0.08860759 0.06329114 0.0000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.05063291 0.000000000 0.012658228 0.0000000000

Customer behavior prediction

We weren’t able to go farther on customer beghavior prediction.

The next steps would have been: 1. Compute the distances between the customers who have purchased items in January and February and each cluster, based on their purchases in January. 2. Use it to assign them to a particular cluster. 3. Use the informations of each cluster to see wht each customer might need and therefore might order next.